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Computing Education in the Era of Generative AI
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Challenges and opportunities faced by computing educators and students adapting to LLMs capable of generating accurate source code from natural-language problem descriptions.
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“Researchers such as Denny et al. [4], Khalil and Er [10], and Susnjak [13] also address the limitations of current plagiarism detection tools, which fail to recognize the original essays produced by ChatGPT, indicating a need for advancements in detection technologies and updated classroom policies.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Several authors lament the current state of popular AI detection tools, such as iThenticate, MOSS, and Turnitin, stating they are inadequate for the needs of educators [4,9,10].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Generative AI, such as GPT-4o CoPilot and OpenAI's GPT-4, enables the generation of sophisticated code from natural-language prompts, raising concerns about student overreliance and potential academic misconduct. This issue is reported by Denny et al. [4].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [4]
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In the citing paper
“Generative AI, such as GPT-4o CoPilot and OpenAI's GPT-4, enables the generation of sophisticated code from natural-language prompts, raising concerns about student overreliance and potential academic misconduct. This issue is reported by Denny et al. [4].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [4]
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“This model helps reconcile the “performance–learning paradox” noted in recent empirical and review work (Yan et al., 2025; Denny et al., 2024).”
From Tool, tutor, or crutch?: A grounded theory of cognitive scaffolding and offloading in AI-assisted programming education (2026) · cites this paper as Denny et al., 2024
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Cites
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · cited 2×
“For example, in a deep discussion of LLMs, Bommasani et al. highlight that it will become “much more complex for teachers to understand the extent of a student’s contribution” and to “regulate ineffective collaborations and detect plagiarism” [7].”
BackgroundEvaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436 · cited 2×
“Can a student with no programming knowledge, but who is armed with a code-generating LLM, pass typical programming assessments?”
BackgroundCompetition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043 · cited 4×
“Similarly, the developers of AlphaCode suggest such tools have “the potential for a positive, transformative impact on society, with a wide range of applications including computer science education” [21].”
BackgroundInfluentialPrompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Reynolds, L., & McDonell, K. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451760 · Cited by 952 · cited 1×
“It is well known that the outputs produced by large language models are very sensitive to their inputs [32].”
BackgroundVaithilingam, P., Zhang, T., & Glassman, E. L. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519665 · Cited by 660 · cited 1×
“Depending on the prompt, the resulting programs used varied programmatic structures, while ultimately favoring expected methods for each problem variation (i.e., for-loops for processing lists, and while-loops for processing standard input).”
BackgroundAutomatic Generation of Programming Exercises and Code Explanations Using Large Language Models
Sarsa, S., Denny, P., Hellas, A., & Leinonen, J. (2022) · Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3501385.3543957 · Cited by 497
Self-citationAsleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions
Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., & Karri, R. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833571 · Cited by 476
The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming
Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022) · Proceedings of the 24th Australasian Computing Education Conference · doi:10.1145/3511861.3511863 · Cited by 463 · cited 6×
“This can facilitate discussions about different approaches and the quality of solutions, and provide opportunities for refactoring exercises [15].”
BackgroundResultInfluentialSelf-citationGrounded Copilot: How Programmers Interact with Code-Generating Models
Barke, S., James, M. B., & Polikarpova, N. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3586030 · Cited by 436 · cited 1×
“Other recent work has shown that developers are more successful working with Copilot when they decompose larger programming statements into smaller tasks and then explicitly prompt Copilot for each of the subtasks [1, 18].”
BackgroundThe Robots Are Here: Navigating the Generative AI Revolution in Computing Education
Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., et al. (2023) · Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3623762.3633499 · Cited by 396 · cited 1×
“For a more technical overview of the historical developments and future trends of language models, we direct the reader to the CACM article by Li [20].”
MethodSelf-citation
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- Porter, L. and Zingaro, D. Learn AI-Assisted Python Programming With GitHub Copilot and ChatGPT. Manning, Shelter Island, NY, USA (2023); https://bit.ly/4aE7CRa
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- Thompson, E., Whalley, J., Lister, R., and Simon, B. Code classification as a learning and assessment exercise for novice programmers. In Proceedings of the 19th Annual Conf. of the National Advisory Committee on Computing Qualifications (2006), 291--298.
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Philbin, C. A., & Sentance, S. (2025) · Conference on United Kingdom & Ireland Computing Education Research (UKICER) · doi:10.1145/3754508.3754513 · Cited by 6
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“GenAI is increasingly being used in higher education, particularly within computing education [1].”
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
Prather, J., Reeves, B., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B., Kimmel, B., Wright, J., & Briggs, B. (2024) · arXiv · Cited by 196
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“Despite widespread adoption of LLMs in programming tasks [9, 10, 12, 15, 21], fewer comparative studies have been conducted between web search and LLM querying in computer science and computer science education.”
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“Beyond comprehension, there’s immense concern about an over-reliance on these tools [7], potentially atrophying students’ ability to code independently [8].”
BackgroundA Comparison of Generative AI Solutions and Textbook Solutions in an Introductory Programming Course
Bekkering, T. J., & Harrington, P. (2024) · Information Systems Education Journal · doi:10.62273/yqwp1758 · Cited by 5
"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering
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Hwang, H., & Lee, J. (2025) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2025.28.2.002 · Cited by 4
Qiao, S.-Y., Denny, P., & Giacaman, N. (2024) · IFAC Symposium on Advances in Control Education · doi:10.1145/3716640.3716651 · Cited by 6
Self-citationAthena: A GenAI-Powered Programming Tutor Based on Open-Source LLM
Lin, Y., Khan, M. F. F., & Sakamura, K. (2025) · IEEE International Conference on Consumer Technology (ICCT-Pacific) · doi:10.1109/icct-pacific63901.2025.11012850 · Cited by 4
“Among programming students it has become a common practice to simply copy and paste quizzes and assignments into AI and use the AI-generated solutions, regardless of understanding or accuracy, bypassing self-reliance [1] [6].”
Stimulating Critical Thinking in a Web Programming Module with Generative AI Tools
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“The challenges of AI in HE and how to teach introductory programming courses were discussed in [9].”
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Blumenthal, R. L. (2025) · ACM Inroads · doi:10.1145/3773089 · Cited by 1
“Much of the educational computer science debate has concentrated on the use of LLMs to support students’ programming skills [12, 14, 17].”
InfluentialFeng, Z., Milewicz, R. M., Murphy-Hill, E. R., Menezes, T., Serebrenik, A., Steinmacher, I., & Sarma, A. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3789210 · Cited by 7
“On the other hand, over-reliance on GenAI also introduces new challenges and risks, such that homogenization of code and problem-solving approaches may reduce opportunities for creative exploration, and weaken interpersonal connections [2, 12, 38].”
Zhou, Y., Babu, P. M., Pandey, U., Magana, A. J., & Li, T. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789139 · Cited by 4
One Line at a Time: Scaffolding Reflective Code Evaluation through Structured AI Assistance
Qiu, V., Parker, L., Riegel, K., Giacaman, N., Denny, P. C., MacNeil, S., & Prather, J. E. (2026) · Australasian Computing Education Conference · doi:10.1145/3786228.3786243 · Cited by 4
Self-citationBlumenthal, R. L. (2026) · ACM Inroads · doi:10.1145/3799901 · Cited by 1
Ouaazki, A., Shibani, A., Knight, S., & Holzer, A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100608 · Cited by 1
Vázquez‐Ingelmo, A., Ruiz, M. P. P., García‐Peñalvo, F. J., & García-Holgado, A. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2676747 · Cited by 1
From algorithms to tutors: tracing the evolution of generative AI in education
Aggarwal, D. (2026) · SN Social Sciences · doi:10.1007/s43545-026-01515-4 · Cited by 1
Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming
Rinja, D., de Oliveira, E. A., López‐Pernas, S., Saqr, M., Specht, M. M., & Misiejuk, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29763-1_35 · Cited by 4
“This distinction is consistent with prior work on pedagogical agents and intelligent tutoring systems, which emphasizes explanation, feedback, and scaffolding as core features of pedagogically meaningful support [6].”
Si, Y., Li, D., Shi, H., & Zhang, J. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3808163 · Cited by 1
Beyond Answer Engines: LLMs as Reasoning Partners in Data Structures and Algorithms Education
Khan, S. Z., Leal, D., Valença, L., Abdellatif, A., Wang, M., Krishnamurthy, D., & Santos, R. D. S. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3786580.3786972 · Cited by 1
Jhangiani, R. S., & Baker, N. (2026) · Psychology Learning & Teaching · doi:10.1177/14757257261467363 · Cited by 1
“Paradoxically, we see persuasive arguments for integrating AI skill development within individual disciplines, whether they be computer science (Denny et al., 2024), teacher education (Chiu et al., 2026), business (Wu et al., 2026), medicine (Singla et al., 2024), linguistics (Pérez-Paredes et al.,…”
Kumar, A., Wongsirichot, T., & Nanthaamornphong, A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100677 · Cited by 2
What is Skill? (and why does it matter?)
McDermott, R., & Daniels, M. (2023) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10343520 · Cited by 2
Sustainability, Ethics and Artificial Intelligence in Computing Education
Moore, S. J., & McCullagh, P. (2024) · 2024 35th Irish Signals and Systems Conference (ISSC) · doi:10.1109/issc61953.2024.10603347 · Cited by 1
Twisted Games: A First Experience of Inclusion of AI tools in First Year Programming Classes
Wightman, P. M. (2024) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci62337.2024.10814750 · Cited by 1
Empowering Computing Students with Large Language Models by Developing an Escape Room Game
Giacaman, N., & Terragni, V. (2025) · ACM eBooks · doi:10.1145/3708897 · Cited by 2
On the Opportunities of Large Language Models for Programming Process Data
Edwards, J., Hellas, A., & Leinonen, J. (2025) · Australasian Computing Education Conference · doi:10.1145/3716640.3716652 · Cited by 1
Self-citationApplying Artificial Intelligence in Software Development Education
Grant, E. S., Shao, S., Shi, Q., & Arinaitwe, M. (2025) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.3390/engproc2025092026 · Cited by 2
Discipline-Sensitive Generative AI in Higher Education
López-López, E. M., Bru-Cordero, O., & Correa-Álvarez, C. D. (2026) · Encyclopedia · doi:10.3390/encyclopedia6060119 · Cited by 4
Wu, M., & Ouyang, F. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10211-x
“Using GenAI pro-ductively requires students to judge whether generated code is correct, and novices hold limited domain knowledge for making that judgment (Prather et al., 2024 ).”
From Topic to Thesis: Critical Engagement with Generative AI in Thesis Preparation
Virkki, O. T., Naeem, U., & Styve, A. (2026) · 2026 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574181
“The third category could be described as conceptual framing, in which attempts are made to map the landscape of the technology's implications, opportunities, and challenges e.g. [6], [23].”
A Scaffolded GenAI Lab in Early Undergraduate CS: A Mixed-Methods, Multi-Course Evaluation
Dickey, E., Bejarano, A., Kuperus, R., & Fagundes, B. (2025) · arXiv · Cited by 9
Can You Spot the AI? Incorporating GenAI into Technical Writing Assignments
Rajabi, P., & Kerslake, C. P. (2024) · Western Canadian Conference on Computing Education · doi:10.1145/3660650.3660673 · Cited by 1
The Seamless Integration of Machine Learning Education into High School Mathematics Classrooms
Oh, H., Guo, R., Xing, W., Liu, Z., Song, Y., & Li, C. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--48137
Zilles, C. B., Zhao, C., Chen, Y., Matthews, E. M., & West, M. (2024) · ACM Virtual Global Computing Education Conference (SIGCSE Virtual) · doi:10.1145/3649165.3703624
“Student cheating has been long documented [4, 12, 15, 19] and the capabilities of large language models [5, 9] facilitate cheating by making it quick, cheap, and something that can be done without interacting with another human.”
Dawson, M. G., Deer, R., & Boguslawski, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5055559
Osti, G., & Roke, E. R. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825858
KernelVM: Teaching Linux Kernel Programming through a Browser-Based Virtual Machine
Wen, E., Ma, S., Denny, P. C., Tempero, E. D., Weber, G., & Yue, Z. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701831
Self-citationMeza, F., Acevedo, O., & González, M. (2024) · IEEE Central America and Panama Convention (CONCAPAN) · doi:10.1109/concapan63470.2024.10933867 · Cited by 1
“Denny et al. used Codex, an artificial intelligence model developed by OpenAI to generate code, to study the potential of LLMs to generate programming exercises [20].”
Amiri, S. M. H., & Islam, M. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5240568 · Cited by 1
A Field Study on the Use of Gen AI to Support Computing Education
Ocay, A. B., & Rodrigo, M. M. T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93724-8_17 · Cited by 1
Levchuk, O. (2025) · Congresso Ibero-Americano em Engenharia de Software (CIbSE) · doi:10.5753/cibse.2025.35313
Metadata
- Authors
- Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J., Luxton-Reilly, A., Reeves, B. N., Santos, E. A., & Sarsa, S.
- Affiliations
- University of Auckland (NZ)
- Abilene Christian University (US)
- University College Dublin (IE)
- Aalto University (FI)
- ORCID
- Venue
- Communications of the ACM
- Published
- 2024-01-25
- Volume
- 67
- Issue
- 2
- Pages
- 56-67
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W4390985631
- Cited by
- 316
- References
- 38
- Citations by year
- 2026: 136 · 2025: 102 · 2024: 70
- FWCI
- 116.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Aaltodoc (Aalto University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- generative AI, large language models, computing education, code generation, natural language programming, programming education, artificial intelligence in education
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